{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:10.949718Z",
     "start_time": "2020-04-22T17:50:10.942079Z"
    }
   },
   "outputs": [],
   "source": [
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:11.107309Z",
     "start_time": "2020-04-22T17:50:11.104212Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy as np\n",
    "import os\n",
    "from time import time\n",
    "import cv2\n",
    "from time import time\n",
    "from keras.utils import Sequence\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:11.272794Z",
     "start_time": "2020-04-22T17:50:11.264441Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.1\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "播放视频示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:15.533714Z",
     "start_time": "2020-04-22T17:50:15.521194Z"
    }
   },
   "outputs": [],
   "source": [
    "X_path = []\n",
    "# folder_path = '/ssd/pyr/RWF-2000/val/Fight/'\n",
    "# 视频路径\n",
    "folder_path = '/ssd/pyr/RWF-2000/val/NonFight/'\n",
    "for file in os.listdir(folder_path):\n",
    "    file_path = os.path.join(folder_path, file)\n",
    "    X_path.append(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:17.571536Z",
     "start_time": "2020-04-22T17:50:17.561866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/ssd/pyr/RWF-2000/val/NonFight/Ob0YCDgS_0.avi'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_path[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调试摄像头"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T07:10:08.421346Z",
     "start_time": "2020-04-21T07:09:19.731270Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ipcam started!\n",
      "ipcam stopped!\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import cv2\n",
    "import time\n",
    "import threading\n",
    "\n",
    "# 接收攝影機串流影像，採用多執行緒的方式，降低緩衝區堆疊圖幀的問題。\n",
    "\n",
    "\n",
    "class ipcamCapture:\n",
    "    def __init__(self, URL):\n",
    "        self.Frame = []\n",
    "        self.status = False\n",
    "        self.isstop = False\n",
    "        \n",
    "\n",
    "        # 攝影機連接。\n",
    "        self.capture = cv2.VideoCapture(URL)\n",
    "        self.width = self.capture.get(cv2.CAP_PROP_FRAME_WIDTH);\n",
    "        self.height = self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT);\n",
    "\n",
    "    def start(self):\n",
    "        # 把程式放進子執行緒，daemon=True 表示該執行緒會隨著主執行緒關閉而關閉。\n",
    "        print('ipcam started!')\n",
    "        threading.Thread(target=self.queryframe, daemon=True, args=()).start()\n",
    "\n",
    "    def stop(self):\n",
    "        # 記得要設計停止無限迴圈的開關。\n",
    "        self.isstop = True\n",
    "        print('ipcam stopped!')\n",
    "\n",
    "    def getframe(self):\n",
    "        # 當有需要影像時，再回傳最新的影像。\n",
    "        return self.status, self.Frame\n",
    "\n",
    "    def queryframe(self):\n",
    "        while (not self.isstop):\n",
    "            self.status, self.Frame = self.capture.read()\n",
    "\n",
    "        self.capture.release()\n",
    "\n",
    "\n",
    "# URL = \"rtsp://admin:admin@192.168.1.1/video.h264\"\n",
    "# URL = \"rtsp://admin:RCDPLD@192.168.0.110:554/h264/ch1/main/av_stream\"\n",
    "URL = 0\n",
    "\n",
    "# 連接攝影機\n",
    "ipcam = ipcamCapture(URL)\n",
    "\n",
    "# 啟動子執行緒\n",
    "ipcam.start()\n",
    "\n",
    "# 暫停1秒，確保影像已經填充\n",
    "time.sleep(1)\n",
    "\n",
    "# 使用無窮迴圈擷取影像，直到按下Esc鍵結束\n",
    "while True:\n",
    "    # 使用 getframe 取得最新的影像\n",
    "    flag,I = ipcam.getframe()\n",
    "    \n",
    "    cv2.namedWindow(\"Image\", cv2.WINDOW_NORMAL)\n",
    "    if flag:\n",
    "        cv2.imshow('Image', I)\n",
    "    if cv2.waitKey(1) == 113:\n",
    "        cv2.destroyAllWindows()\n",
    "        ipcam.stop()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T06:17:02.238597Z",
     "start_time": "2020-04-20T06:16:36.566971Z"
    }
   },
   "outputs": [],
   "source": [
    "# from threading import Thread\n",
    "# import cv2, time\n",
    "\n",
    "# class VideoStreamWidget(object):\n",
    "#     def __init__(self, src=0):\n",
    "#         self.capture = cv2.VideoCapture(src)\n",
    "#         # Start the thread to read frames from the video stream\n",
    "#         self.thread = Thread(target=self.update, args=())\n",
    "#         self.thread.daemon = True\n",
    "#         self.thread.start()\n",
    "\n",
    "#     def update(self):\n",
    "#         # Read the next frame from the stream in a different thread\n",
    "#         while True:\n",
    "#             if self.capture.isOpened():\n",
    "#                 (self.status, self.frame) = self.capture.read()\n",
    "#             time.sleep(.01)\n",
    "\n",
    "#     def show_frame(self):\n",
    "#         # Display frames in main program\n",
    "#         cv2.namedWindow(\"frame\", cv2.WINDOW_NORMAL)\n",
    "#         cv2.imshow('frame', self.frame)\n",
    "#         if cv2.waitKey(1) == 113:\n",
    "#             self.capture.release()\n",
    "#             cv2.destroyAllWindows()\n",
    "#             exit(1)\n",
    "\n",
    "# # if __name__ == '__main__':\n",
    "# URL = \"rtsp://admin:RCDPLD@192.168.0.110:554/h264/ch1/main/av_stream\"\n",
    "# video_stream_widget = VideoStreamWidget(URL)\n",
    "# while True:\n",
    "#     try:\n",
    "#         video_stream_widget.show_frame()\n",
    "#     except AttributeError:\n",
    "#         pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-19T11:43:29.144832Z",
     "start_time": "2020-04-19T11:43:03.513900Z"
    }
   },
   "outputs": [],
   "source": [
    "path = X_path[1]\n",
    "ip = 'rtsp://admin:RCDPLD@192.168.0.110:554/h264/ch1/main/av_stream'\n",
    "\n",
    "capture = cv2.VideoCapture(ip)\n",
    "capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))\n",
    "FPS = capture.get(cv2.CAP_PROP_FPS)\n",
    "if FPS == 0:\n",
    "    FPS = 1000\n",
    "\n",
    "delay = int(1000 / FPS)\n",
    "    \n",
    "i = 0\n",
    "\n",
    "while capture.isOpened():\n",
    "    i += 1\n",
    "    ret, prev = capture.read()\n",
    "    if ret == True:\n",
    "        if i % 3 == 0:\n",
    "            cv2.putText(prev, \"Hello World!\", (400, 50),\n",
    "                        cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), 2)\n",
    "        cv2.namedWindow(\"video\", cv2.WINDOW_NORMAL)\n",
    "        cv2.imshow('video', prev)\n",
    "    else:\n",
    "        break\n",
    "    if cv2.waitKey(delay) & 0xFF == 113:  # 按'q'退出，按esc退出有问题\n",
    "        break\n",
    "        \n",
    "\n",
    "cv2.destroyAllWindows()\n",
    "capture.release()\n",
    "i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "\n",
    "\n",
    "img_save_dir = \"/home/arkenstone/Pictures/Webcam/chessboard_cali_1280_960\"\n",
    "if not os.path.exists(img_save_dir):\n",
    "    os.makedirs(img_save_dir)\n",
    "\n",
    "source = \"rtsp://admin:startdt123@192.168.1.64/Streaming/Channels/1\"\n",
    "cam = cv2.VideoCapture(source)\n",
    "img_counter = 0\n",
    "while(cam.isOpened()):\n",
    "    ret, frame = cam.read()\n",
    "    cv2.imshow('frame', frame)\n",
    "    if not ret:\n",
    "        break\n",
    "    # press ESC to escape (ESC ASCII value: 27)\n",
    "    if cv2.waitKey(1) & 0xFF == 113:\n",
    "        break\n",
    "    # press Space to capture image (Space ASCII value: 32)\n",
    "    elif cv2.waitKey(1) & 0xFF == 32:\n",
    "        print \"Saving image ...\"\n",
    "        img_file = img_save_dir + \"/opencv_frame_{}.jpg\".format(img_counter)\n",
    "        cv2.imwrite(img_file, frame)\n",
    "        print \"WebCam Image {}: {} written!\".format(img_counter, img_file)\n",
    "        img_counter += 1\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "cam.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:40.285635Z",
     "start_time": "2020-04-22T17:50:31.473433Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From /root/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "    \n",
    "model = load_model('weights-improvement-06-0.72.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 双显卡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:32:48.158454Z",
     "start_time": "2020-04-20T08:32:48.156866Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1\"\n",
    "\n",
    "# from keras.utils import multi_gpu_model \n",
    "# parallel_model = multi_gpu_model(model, gpus=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-22T17:50:46.205029Z",
     "start_time": "2020-04-22T17:50:46.170551Z"
    }
   },
   "outputs": [],
   "source": [
    "def normalize(data):\n",
    "    mean = np.mean(data)\n",
    "    std = np.std(data)\n",
    "    return (data-mean) / std\n",
    "\n",
    "\n",
    "def uniform_sampling(video, target_frames=20):\n",
    "    # get total frames of input video and calculate sampling interval\n",
    "    len_frames = int(len(video))  # 计算传入视频长度\n",
    "    interval = int(np.ceil(len_frames/target_frames))  # 求帧间隔，向上取整\n",
    "    # init empty list for sampled video and\n",
    "    sampled_video = []  # 抽帧后的视频\n",
    "    for i in range(0, len_frames, interval):\n",
    "        sampled_video.append(video[i])\n",
    "    # calculate numer of padded frames and fix it\n",
    "    num_pad = target_frames - len(sampled_video)  # 如果总帧数不足，则进行补帧\n",
    "    if num_pad > 0:\n",
    "        padding = [video[i] for i in range(-num_pad, 0)]\n",
    "        sampled_video += padding\n",
    "    # get sampled video\n",
    "    return np.array(sampled_video, dtype=np.float32)\n",
    "\n",
    "\n",
    "def load_data(path):\n",
    "    # load a video to a list of images\n",
    "    data = []\n",
    "    cap = cv2.VideoCapture(path)\n",
    "    length = 50\n",
    "    for i in range(length):  # 读取前75帧，取25-75帧，共50帧,2秒，采样20帧\n",
    "        ret, frame = cap.read()\n",
    "        if ret:\n",
    "            frame = cv2.resize(frame, (224, 224))\n",
    "            data.append(frame)  # 将所有帧存储到data\n",
    "    # compute difference\n",
    "    new_data = [data[0]]  # 取出第一帧，(1, 224, 224, 3)\n",
    "    for i in range(1, len(data)):  # 逐帧计算帧差，(50, 224, 224, 3)\n",
    "        new_data.append(data[i]-data[i-1])\n",
    "    # transfer the list of images to a tensor, and sample fixed numer of frames from original video\n",
    "    new_data = np.array(new_data).astype(np.float32)\n",
    "    new_data = uniform_sampling(video=new_data, target_frames=20)\n",
    "    # normalization\n",
    "    new_data = normalize(new_data)  # 对取得的帧数进行正则化\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 拆解load_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:32:48.318876Z",
     "start_time": "2020-04-20T08:32:48.239998Z"
    }
   },
   "outputs": [],
   "source": [
    "# path = X_path[1]\n",
    "\n",
    "# # input_data = load_data(path)\n",
    "# data = []\n",
    "# cap = cv2.VideoCapture(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:32:48.433624Z",
     "start_time": "2020-04-20T08:32:48.332442Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# input_data = input_data[np.newaxis,...]\n",
    "# input_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:32:48.491940Z",
     "start_time": "2020-04-20T08:32:48.447424Z"
    }
   },
   "outputs": [],
   "source": [
    "# model.predict(input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:32:48.701023Z",
     "start_time": "2020-04-20T08:32:48.506181Z"
    }
   },
   "outputs": [],
   "source": [
    "# path = X_path[1]\n",
    "\n",
    "# capture = cv2.VideoCapture(path)\n",
    "# FPS = capture.get(cv2.CAP_PROP_FPS)\n",
    "# delay = int(1000 / FPS)\n",
    "# i = 0\n",
    "\n",
    "# while capture.isOpened():\n",
    "#     i += 1\n",
    "#     ret, prev = capture.read()\n",
    "#     if ret == True:\n",
    "#             cv2.putText(prev, \"Hello World!\", (400, 50),\n",
    "#                         cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), 2)\n",
    "#         cv2.imshow('video', prev)\n",
    "#     else:\n",
    "#         break\n",
    "#     if cv2.waitKey(delay) & 0xFF == 113:  # 按'q'退出，按esc退出有问题\n",
    "#         break\n",
    "        \n",
    "\n",
    "# cv2.destroyAllWindows()\n",
    "# capture.release()\n",
    "# i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:33:52.794944Z",
     "start_time": "2020-04-20T08:33:52.791340Z"
    }
   },
   "outputs": [],
   "source": [
    "# 路径选择\n",
    "X_path = []\n",
    "# folder_path = '/ssd/pyr/RWF-2000/val/Fight/'\n",
    "folder_path = '/ssd/pyr/RWF-2000/val/NonFight/'\n",
    "for file in os.listdir(folder_path):\n",
    "    file_path = os.path.join(folder_path, file)\n",
    "    X_path.append(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 视频测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T08:43:06.535013Z",
     "start_time": "2020-04-20T08:43:01.574570Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated FPS : 4249.548125633232 \n",
      "Estimated FPS : 4279.902040816327 \n",
      "Estimated FPS : 4524.599784250269 \n",
      "[[0.00995759 0.99004245]]\n",
      "判断 3\n",
      "Estimated FPS : 28.897536239872128 \n",
      "Estimated FPS : 4391.941361256545 \n",
      "Estimated FPS : 4337.439503619442 \n",
      "[[0.01462295 0.985377  ]]\n",
      "判断 6\n",
      "Estimated FPS : 44.39591426303255 \n",
      "Estimated FPS : 4410.414300736067 \n",
      "Estimated FPS : 4429.043294614572 \n",
      "[[0.02238817 0.9776119 ]]\n",
      "判断 9\n",
      "Estimated FPS : 44.413308202206736 \n",
      "Estimated FPS : 4373.62252346194 \n",
      "Estimated FPS : 4447.83032873807 \n",
      "[[0.02711982 0.9728802 ]]\n",
      "判断 12\n",
      "Estimated FPS : 45.39977918732276 \n",
      "Estimated FPS : 4424.371308016877 \n",
      "Estimated FPS : 4500.326180257511 \n",
      "[[0.06025829 0.9397417 ]]\n",
      "判断 15\n",
      "Estimated FPS : 46.23147127551695 \n",
      "Estimated FPS : 4249.548125633232 \n",
      "Estimated FPS : 4466.777422790203 \n",
      "[[0.01862853 0.98137146]]\n",
      "判断 18\n",
      "Estimated FPS : 43.555463249496356 \n",
      "Estimated FPS : 4253.858012170385 \n",
      "Estimated FPS : 4438.4169312169315 \n",
      "[[0.01478825 0.98521173]]\n",
      "判断 21\n",
      "Estimated FPS : 45.383077256005194 \n",
      "Estimated FPS : 4401.158447009444 \n",
      "Estimated FPS : 4346.429015544041 \n",
      "[[0.01449876 0.9855012 ]]\n",
      "判断 24\n",
      "Estimated FPS : 43.65610558308006 \n",
      "Estimated FPS : 4401.158447009444 \n",
      "Estimated FPS : 4443.118644067797 \n",
      "[[0.01369676 0.98630327]]\n",
      "判断 27\n",
      "Estimated FPS : 43.63839151017011 \n",
      "Estimated FPS : 4341.929606625259 \n",
      "Estimated FPS : 3945.7234242709314 \n",
      "[[0.01003057 0.98996943]]\n",
      "判断 30\n",
      "Estimated FPS : 45.18458190593153 \n",
      "Estimated FPS : 4156.891972249752 \n",
      "Estimated FPS : 4355.455867082035 \n",
      "[[0.00776552 0.99223447]]\n",
      "判断 33\n",
      "Estimated FPS : 43.08965573922066 \n",
      "Estimated FPS : 4364.520291363164 \n",
      "Estimated FPS : 4391.941361256545 \n",
      "[[0.0106915 0.9893085]]\n",
      "判断 36\n",
      "Estimated FPS : 44.01112265348737 \n",
      "Estimated FPS : 4136.394477317554 \n",
      "Estimated FPS : 4373.62252346194 \n",
      "[[0.008709   0.99129105]]\n",
      "判断 39\n",
      "Estimated FPS : 45.76386509694384 \n",
      "Estimated FPS : 3672.77057793345 \n",
      "Estimated FPS : 4500.326180257511 \n",
      "[[0.01161809 0.9883819 ]]\n",
      "判断 42\n",
      "Estimated FPS : 44.817643664650696 \n",
      "Estimated FPS : 4148.6686449060335 \n",
      "Estimated FPS : 4457.283740701381 \n",
      "[[0.0116586 0.9883414]]\n",
      "判断 45\n",
      "Estimated FPS : 44.845436660679155 \n",
      "Estimated FPS : 4036.8662175168433 \n",
      "Estimated FPS : 4462.025531914894 \n",
      "[[0.01422156 0.98577845]]\n",
      "判断 48\n",
      "Estimated FPS : 41.59530326470705 \n",
      "Estimated FPS : 3912.597014925373 \n",
      "Estimated FPS : 4240.954499494439 \n",
      "[[0.02309149 0.9769085 ]]\n",
      "判断 51\n",
      "Estimated FPS : 43.32242604528177 \n",
      "Estimated FPS : 4262.504065040651 \n",
      "Estimated FPS : 4429.043294614572 \n",
      "[[0.01699139 0.98300856]]\n",
      "判断 54\n",
      "Estimated FPS : 44.61883131389424 \n",
      "Estimated FPS : 4396.545073375262 \n",
      "Estimated FPS : 4429.043294614572 \n",
      "[[0.03975821 0.9602418 ]]\n",
      "判断 57\n",
      "Estimated FPS : 45.676649315008824 \n",
      "Estimated FPS : 4013.688038277512 \n",
      "Estimated FPS : 4156.891972249752 \n",
      "[[0.00625788 0.99374217]]\n",
      "判断 60\n",
      "Estimated FPS : 45.256250067437065 \n",
      "Estimated FPS : 4206.924774322969 \n",
      "Estimated FPS : 4337.439503619442 \n",
      "[[0.03815697 0.961843  ]]\n",
      "判断 63\n",
      "Estimated FPS : 43.923553004995235 \n",
      "Estimated FPS : 4419.709167544784 \n",
      "Estimated FPS : 4481.0940170940175 \n",
      "[[0.03145609 0.9685439 ]]\n",
      "判断 66\n",
      "Estimated FPS : 45.60364454785643 \n",
      "Estimated FPS : 4378.187891440501 \n",
      "Estimated FPS : 4405.781512605042 \n",
      "[[0.0286111  0.97138894]]\n",
      "判断 69\n",
      "Estimated FPS : 44.36351328481977 \n",
      "Estimated FPS : 4410.414300736067 \n",
      "Estimated FPS : 4288.654396728017 \n",
      "[[0.03386632 0.96613365]]\n",
      "判断 72\n",
      "Estimated FPS : 44.227850770820595 \n",
      "Estimated FPS : 4346.429015544041 \n",
      "Estimated FPS : 4476.311632870865 \n",
      "[[0.01603602 0.983964  ]]\n",
      "判断 75\n",
      "Estimated FPS : 44.216660692825066 \n",
      "Estimated FPS : 4293.0440122824975 \n",
      "Estimated FPS : 4438.4169312169315 \n",
      "[[0.08475841 0.91524154]]\n",
      "判断 78\n",
      "Estimated FPS : 44.11620421987084 \n",
      "Estimated FPS : 4319.5715756951595 \n",
      "Estimated FPS : 4382.762800417973 \n",
      "[[0.01210728 0.9878927 ]]\n",
      "判断 81\n",
      "Estimated FPS : 42.98147237252009 \n",
      "Estimated FPS : 4364.520291363164 \n",
      "Estimated FPS : 4144.569169960474 \n",
      "[[0.01120974 0.9887903 ]]\n",
      "判断 84\n",
      "Estimated FPS : 43.214853127543606 \n",
      "Estimated FPS : 4328.487100103199 \n",
      "Estimated FPS : 4301.850256410256 \n",
      "[[0.01911533 0.9808847 ]]\n",
      "判断 87\n",
      "Estimated FPS : 44.037924046911584 \n",
      "Estimated FPS : 4401.158447009444 \n",
      "Estimated FPS : 3968.1210974456008 \n",
      "[[0.01270027 0.98729974]]\n",
      "判断 90\n",
      "Estimated FPS : 46.30956928818275 \n",
      "Estimated FPS : 4373.62252346194 \n",
      "Estimated FPS : 4447.83032873807 \n",
      "[[0.02491358 0.9750864 ]]\n",
      "判断 93\n",
      "Estimated FPS : 45.34480745529633 \n",
      "Estimated FPS : 4068.1901066925316 \n",
      "Estimated FPS : 4355.455867082035 \n",
      "[[0.00518148 0.9948185 ]]\n",
      "判断 96\n",
      "Estimated FPS : 46.333101353217344 \n",
      "Estimated FPS : 4228.129032258064 \n",
      "Estimated FPS : 4017.532567049808 \n",
      "[[0.02290566 0.97709435]]\n",
      "判断 99\n",
      "Estimated FPS : 44.75977248231189 \n",
      "Estimated FPS : 4337.439503619442 \n",
      "Estimated FPS : 4324.024742268041 \n",
      "[[0.02042068 0.9795793 ]]\n",
      "判断 102\n",
      "Estimated FPS : 43.99358080114119 \n",
      "Estimated FPS : 4410.414300736067 \n",
      "Estimated FPS : 4297.44262295082 \n",
      "[[0.02150979 0.9784902 ]]\n",
      "判断 105\n",
      "Estimated FPS : 45.06660649625547 \n",
      "Estimated FPS : 4144.569169960474 \n",
      "Estimated FPS : 4355.455867082035 \n",
      "[[0.01427222 0.9857277 ]]\n",
      "判断 108\n",
      "Estimated FPS : 45.407151595197625 \n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# scaler = StandardScaler()\n",
    "path = X_path[1]\n",
    "\n",
    "capture = cv2.VideoCapture(path)\n",
    "FPS = capture.get(cv2.CAP_PROP_FPS)\n",
    "delay = int(1000 / FPS)\n",
    "i = 0\n",
    "data_list = []\n",
    "raw_data_list = []  # 网络每次输入的第一帧为原始图像\n",
    "while capture.isOpened():\n",
    "    ret, frame = capture.read()\n",
    "    start = time.time()\n",
    "    if ret == True:\n",
    "        # 处理帧\n",
    "        frame = cv2.resize(frame, (224, 224))\n",
    "        if i == 0:  # 第一帧直接放入\n",
    "            data_list.append(frame)\n",
    "        elif (i+1) % 3 == 0:  # 缓存第3i-1帧\n",
    "            temp_frame = frame\n",
    "        elif i % 3 == 0:  # 每3帧取一次，减去前一帧\n",
    "            # 保留原始帧作为后续输入的第一帧\n",
    "            raw_data_list.append(frame)\n",
    "            data_list.append(frame - temp_frame)\n",
    "        # print(len(data_list))\n",
    "        # 当data放满一组4帧，开始处理数据\n",
    "        if len(data_list)==2:\n",
    "            # 处理数据\n",
    "            np_data = np.array(data_list).astype(np.float32)\n",
    "            norm_data = normalize(np_data)\n",
    "            input_data = norm_data[np.newaxis, ...]\n",
    "#             input_data = np_data[np.newaxis, ...]\n",
    "            # 输入到网络\n",
    "            print(model.predict(input_data))\n",
    "            print(\"判断\",i)\n",
    "            data_list.pop(0)\n",
    "            data_list[0] = raw_data_list.pop(0)\n",
    "        \n",
    "#         if i%25==0:\n",
    "        end = time.time()\n",
    "        seconds = end - start\n",
    "        fps = 1/seconds\n",
    "        cv2.namedWindow(\"video\", cv2.WINDOW_NORMAL)\n",
    "#         cv2.putText(frame, \"Hello World!\", (0, 50),\n",
    "#                     cv2.FONT_HERSHEY_PLAIN, 2.0, (0, 0, 255), 2)\n",
    "        cv2.imshow('video', frame)\n",
    "#         print(\"Estimated s : {0} \".format(seconds))\n",
    "        print(\"Estimated FPS : {0} \".format(fps))\n",
    "#         print(\"Real FPS : {0} \".format(FPS))\n",
    "        i += 1\n",
    "    else:\n",
    "        break\n",
    "    if cv2.waitKey(int(max(delay-seconds, 1))) & 0xFF == 113:  # 按'q'退出，按esc退出有问题\n",
    "        break\n",
    "\n",
    "\n",
    "cv2.destroyAllWindows()\n",
    "capture.release()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 摄像头测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T09:23:23.404542Z",
     "start_time": "2020-04-20T09:22:57.190832Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ipcam started!\n",
      "[[0.5340873  0.46591267]]\n",
      "判断 3\n",
      "[[0.5304096  0.46959046]]\n",
      "判断 6\n",
      "[[0.53347623 0.46652377]]\n",
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      "[[0.53347623 0.46652377]]\n",
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      "[[0.5340411  0.46595886]]\n",
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      "[[0.5406986  0.45930135]]\n",
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      "[[0.5433462  0.45665383]]\n",
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      "[[0.5433462  0.45665383]]\n",
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      "[[0.54586923 0.45413077]]\n",
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      "[[0.54586923 0.45413077]]\n",
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      "[[0.54586923 0.45413077]]\n",
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      "[[0.546131 0.453869]]\n",
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      "[[0.546131 0.453869]]\n",
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      "[[0.54366136 0.45633867]]\n",
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      "[[0.54366136 0.45633867]]\n",
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      "[[0.542208   0.45779195]]\n",
      "判断 48\n",
      "[[0.54345626 0.4565437 ]]\n",
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      "[[0.54345626 0.4565437 ]]\n",
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      "[[0.54345626 0.4565437 ]]\n",
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      "[[0.5347805  0.46521944]]\n",
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      "[[0.5340784 0.4659216]]\n",
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      "[[0.8867503  0.11324967]]\n",
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      "[[0.70993537 0.2900646 ]]\n",
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      "[[0.9194204  0.08057961]]\n",
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      "[[0.50499654 0.4950034 ]]\n",
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      "[[0.50499654 0.4950034 ]]\n",
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      "[[0.5073579  0.49264213]]\n",
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      "[[0.80577224 0.19422771]]\n",
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      "[[0.644509   0.35549095]]\n",
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      "[[0.5061458 0.4938542]]\n",
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      "[[0.5046769  0.49532315]]\n",
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      "[[0.5010863  0.49891374]]\n",
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      "[[0.5114236  0.48857644]]\n",
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      "[[0.51582056 0.48417947]]\n",
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      "[[0.5174729  0.48252708]]\n",
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      "[[0.5174729  0.48252708]]\n",
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      "[[0.51453817 0.48546183]]\n",
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      "[[0.51453817 0.48546183]]\n",
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      "[[0.51453817 0.48546183]]\n",
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      "[[0.50765157 0.49234846]]\n",
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      "[[0.50765157 0.49234846]]\n",
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      "[[0.5063292  0.49367082]]\n",
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      "[[0.5063292  0.49367082]]\n",
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      "[[0.5063292  0.49367082]]\n",
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      "[[0.5063292  0.49367082]]\n",
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      "[[0.5063292  0.49367082]]\n",
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      "[[0.52377677 0.47622323]]\n",
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      "[[0.52377677 0.47622323]]\n",
      "判断 216\n",
      "[[0.52377677 0.47622323]]\n",
      "判断 219\n",
      "[[0.51414186 0.48585817]]\n",
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      "[[0.51414186 0.48585817]]\n",
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      "[[0.51414186 0.48585817]]\n",
      "判断 228\n",
      "[[0.5229227  0.47707728]]\n",
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      "[[0.5229227  0.47707728]]\n",
      "判断 234\n",
      "[[0.52618736 0.47381264]]\n",
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      "[[0.52618736 0.47381264]]\n",
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      "[[0.52618736 0.47381264]]\n",
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      "[[0.52618736 0.47381264]]\n",
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      "[[0.52618736 0.47381264]]\n",
      "判断 249\n",
      "[[0.5288557  0.47114432]]\n",
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      "[[0.5288557  0.47114432]]\n",
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      "[[0.5435018  0.45649815]]\n",
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      "[[0.5435018  0.45649815]]\n",
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      "[[0.5435018  0.45649815]]\n",
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      "[[0.54290897 0.457091  ]]\n",
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      "[[0.54290897 0.457091  ]]\n",
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      "[[0.74436456 0.25563544]]\n",
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      "[[0.5468448  0.45315522]]\n",
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      "[[0.5468448  0.45315522]]\n",
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      "[[0.54769063 0.45230943]]\n",
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      "[[0.54769063 0.45230943]]\n",
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      "[[0.5444523 0.4555477]]\n",
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      "[[0.8565643  0.14343575]]\n",
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      "[[0.5513109 0.4486891]]\n",
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      "[[0.5513109 0.4486891]]\n",
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      "[[0.5513109 0.4486891]]\n",
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      "[[0.55495346 0.44504654]]\n",
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      "[[0.55495346 0.44504654]]\n",
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      "[[0.55495346 0.44504654]]\n",
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      "[[0.55309033 0.44690964]]\n",
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      "[[0.55309033 0.44690964]]\n",
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      "[[0.76909524 0.23090473]]\n",
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      "[[0.5529141  0.44708595]]\n",
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      "[[0.55352294 0.44647703]]\n",
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      "[[0.55352294 0.44647703]]\n",
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      "[[0.55352294 0.44647703]]\n",
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      "[[0.55363846 0.4463615 ]]\n",
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      "[[0.55363846 0.4463615 ]]\n",
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      "[[0.55363846 0.4463615 ]]\n",
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      "[[0.55245435 0.44754562]]\n",
      "判断 390\n",
      "[[0.5461936  0.45380646]]\n",
      "判断 393\n",
      "[[0.5461936  0.45380646]]\n",
      "判断 396\n",
      "[[0.5429064  0.45709357]]\n",
      "判断 399\n",
      "[[0.5429064  0.45709357]]\n",
      "判断 402\n",
      "[[0.54307336 0.4569266 ]]\n",
      "判断 405\n",
      "[[0.53888696 0.46111298]]\n",
      "判断 408\n",
      "[[0.53888696 0.46111298]]\n",
      "判断 411\n",
      "[[0.53888696 0.46111298]]\n",
      "判断 414\n",
      "[[0.532827   0.46717298]]\n",
      "判断 417\n",
      "[[0.532827   0.46717298]]\n",
      "判断 420\n",
      "[[0.5308915 0.4691085]]\n",
      "判断 423\n",
      "[[0.5308915 0.4691085]]\n",
      "判断 426\n",
      "[[0.53023124 0.46976876]]\n",
      "判断 429\n",
      "[[0.53023124 0.46976876]]\n",
      "判断 432\n",
      "[[0.53023124 0.46976876]]\n",
      "判断 435\n",
      "[[0.52350354 0.47649643]]\n",
      "判断 438\n",
      "[[0.52350354 0.47649643]]\n",
      "判断 441\n",
      "[[0.75740266 0.24259733]]\n",
      "判断 444\n",
      "[[0.5236754  0.47632462]]\n",
      "判断 447\n",
      "[[0.5236754  0.47632462]]\n",
      "判断 450\n",
      "[[0.5236754  0.47632462]]\n",
      "判断 453\n",
      "[[0.5236754  0.47632462]]\n",
      "判断 456\n",
      "[[0.52940893 0.4705911 ]]\n",
      "判断 459\n",
      "[[0.52940893 0.4705911 ]]\n",
      "判断 462\n",
      "[[0.53372675 0.46627322]]\n",
      "判断 465\n",
      "[[0.53372675 0.46627322]]\n",
      "判断 468\n",
      "[[0.5316028 0.4683972]]\n",
      "判断 471\n",
      "[[0.5302723  0.46972764]]\n",
      "判断 474\n",
      "[[0.5302723  0.46972764]]\n",
      "判断 477\n",
      "[[0.5302723  0.46972764]]\n",
      "判断 480\n",
      "[[0.5302723  0.46972764]]\n",
      "判断 483\n",
      "[[0.52992636 0.47007367]]\n",
      "判断 486\n",
      "[[0.52992636 0.47007367]]\n",
      "判断 489\n",
      "[[0.5282325  0.47176751]]\n",
      "判断 492\n",
      "[[0.5278063 0.4721937]]\n",
      "判断 495\n",
      "[[0.5278063 0.4721937]]\n",
      "判断 498\n",
      "[[0.5278063 0.4721937]]\n",
      "判断 501\n",
      "[[0.5278063 0.4721937]]\n",
      "判断 504\n",
      "[[0.52863026 0.47136977]]\n",
      "判断 507\n",
      "[[0.5285504  0.47144967]]\n",
      "判断 510\n",
      "[[0.5285504  0.47144967]]\n",
      "判断 513\n",
      "[[0.528787   0.47121298]]\n",
      "判断 516\n",
      "[[0.528787   0.47121298]]\n",
      "判断 519\n",
      "[[0.528787   0.47121298]]\n",
      "判断 522\n",
      "[[0.52996725 0.4700327 ]]\n",
      "判断 525\n",
      "[[0.52996725 0.4700327 ]]\n",
      "判断 528\n",
      "[[0.6784614 0.3215386]]\n",
      "判断 531\n",
      "[[0.5281028  0.47189716]]\n",
      "判断 534\n",
      "[[0.5281028  0.47189716]]\n",
      "判断 537\n",
      "[[0.5281028  0.47189716]]\n",
      "判断 540\n",
      "[[0.5281028  0.47189716]]\n",
      "判断 543\n",
      "[[0.527975   0.47202498]]\n",
      "判断 546\n",
      "[[0.91494656 0.08505344]]\n",
      "判断 549\n",
      "[[0.53270745 0.46729255]]\n",
      "判断 552\n",
      "[[0.53270745 0.46729255]]\n",
      "判断 555\n",
      "[[0.5277293  0.47227076]]\n",
      "判断 558\n",
      "[[0.5277293  0.47227076]]\n",
      "判断 561\n",
      "[[0.5277257  0.47227436]]\n",
      "判断 564\n",
      "[[0.5277257  0.47227436]]\n",
      "判断 567\n",
      "[[0.5277257  0.47227436]]\n",
      "判断 570\n",
      "[[0.5238856 0.4761144]]\n",
      "判断 573\n",
      "[[0.5238856 0.4761144]]\n",
      "判断 576\n",
      "[[0.9763896  0.02361048]]\n",
      "判断 579\n",
      "[[0.5236376  0.47636238]]\n",
      "判断 582\n",
      "[[0.5236376  0.47636238]]\n",
      "判断 585\n",
      "[[0.5236376  0.47636238]]\n",
      "判断 588\n",
      "[[0.5236376  0.47636238]]\n",
      "判断 591\n",
      "[[0.52413404 0.475866  ]]\n",
      "判断 594\n",
      "[[0.52182853 0.47817144]]\n",
      "判断 597\n",
      "[[0.52182853 0.47817144]]\n",
      "判断 600\n",
      "[[0.522361 0.477639]]\n",
      "判断 603\n",
      "[[0.522361 0.477639]]\n",
      "判断 606\n",
      "[[0.70281786 0.2971822 ]]\n",
      "判断 609\n",
      "[[0.52368987 0.47631016]]\n",
      "判断 612\n",
      "[[0.52368987 0.47631016]]\n",
      "判断 615\n",
      "[[0.5253271  0.47467294]]\n",
      "判断 618\n",
      "[[0.5253271  0.47467294]]\n",
      "判断 621\n",
      "[[0.5233302 0.4766698]]\n",
      "判断 624\n",
      "[[0.5233302 0.4766698]]\n",
      "判断 627\n",
      "[[0.5233302 0.4766698]]\n",
      "判断 630\n",
      "[[0.52415174 0.47584826]]\n",
      "判断 633\n",
      "[[0.52415174 0.47584826]]\n",
      "判断 636\n",
      "[[0.89360034 0.10639969]]\n",
      "判断 639\n",
      "[[0.5296864 0.4703136]]\n",
      "判断 642\n",
      "[[0.5296864 0.4703136]]\n",
      "判断 645\n",
      "[[0.5412995  0.45870048]]\n",
      "判断 648\n",
      "[[0.539893   0.46010703]]\n",
      "判断 651\n",
      "[[0.54089355 0.4591064 ]]\n",
      "判断 654\n",
      "[[0.54089355 0.4591064 ]]\n",
      "判断 657\n",
      "[[0.54047614 0.45952383]]\n",
      "判断 660\n",
      "[[0.54047614 0.45952383]]\n",
      "判断 663\n",
      "[[0.54047614 0.45952383]]\n",
      "判断 666\n",
      "[[0.54112893 0.4588711 ]]\n",
      "判断 669\n",
      "[[0.54112893 0.4588711 ]]\n",
      "判断 672\n",
      "[[0.53809255 0.46190748]]\n",
      "判断 675\n",
      "[[0.53769904 0.46230102]]\n",
      "判断 678\n",
      "[[0.53769904 0.46230102]]\n",
      "判断 681\n",
      "[[0.53769904 0.46230102]]\n",
      "判断 684\n",
      "[[0.53769904 0.46230102]]\n",
      "判断 687\n",
      "[[0.5412466  0.45875344]]\n",
      "判断 690\n",
      "[[0.54891855 0.45108145]]\n",
      "判断 693\n",
      "[[0.54891855 0.45108145]]\n",
      "判断 696\n",
      "[[0.5378153  0.46218476]]\n",
      "判断 699\n",
      "[[0.5378153  0.46218476]]\n",
      "判断 702\n",
      "[[0.5378153  0.46218476]]\n",
      "判断 705\n",
      "[[0.5351231  0.46487686]]\n",
      "判断 708\n",
      "[[0.5351231  0.46487686]]\n",
      "判断 711\n",
      "[[0.9816135  0.01838643]]\n",
      "判断 714\n",
      "[[0.533594 0.466406]]\n",
      "判断 717\n",
      "[[0.533594 0.466406]]\n",
      "判断 720\n",
      "[[0.533594 0.466406]]\n",
      "判断 723\n",
      "[[0.533594 0.466406]]\n",
      "判断 726\n",
      "[[0.53326374 0.46673626]]\n",
      "判断 729\n",
      "[[0.53326374 0.46673626]]\n",
      "判断 732\n",
      "[[0.5315795  0.46842057]]\n",
      "判断 735\n",
      "[[0.5315795  0.46842057]]\n",
      "判断 738\n",
      "[[0.52793634 0.47206366]]\n",
      "判断 741\n",
      "[[0.5284075  0.47159252]]\n",
      "判断 744\n",
      "[[0.5284075  0.47159252]]\n",
      "判断 747\n",
      "[[0.5284075  0.47159252]]\n",
      "判断 750\n",
      "[[0.5284075  0.47159252]]\n",
      "判断 753\n",
      "[[0.52651113 0.47348884]]\n",
      "判断 756\n",
      "[[0.5356977  0.46430233]]\n",
      "判断 759\n",
      "[[0.5356977  0.46430233]]\n",
      "判断 762\n",
      "[[0.5356977  0.46430233]]\n",
      "判断 765\n",
      "[[0.5337662 0.4662338]]\n",
      "判断 768\n",
      "[[0.5337662 0.4662338]]\n",
      "判断 771\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5328229  0.46717706]]\n",
      "判断 774\n",
      "[[0.5328229  0.46717706]]\n",
      "判断 777\n",
      "[[0.5302782  0.46972173]]\n",
      "判断 780\n",
      "[[0.5302782  0.46972173]]\n",
      "判断 783\n",
      "[[0.5302782  0.46972173]]\n",
      "判断 786\n",
      "[[0.5322155  0.46778452]]\n",
      "判断 789\n",
      "[[0.5322155  0.46778452]]\n",
      "判断 792\n",
      "[[0.528939 0.471061]]\n",
      "判断 795\n",
      "[[0.528939 0.471061]]\n",
      "判断 798\n",
      "[[0.528939 0.471061]]\n",
      "判断 801\n",
      "[[0.5303483  0.46965173]]\n",
      "判断 804\n",
      "[[0.5303483  0.46965173]]\n",
      "判断 807\n",
      "[[0.8628086  0.13719139]]\n",
      "判断 810\n",
      "[[0.5242148  0.47578517]]\n",
      "判断 813\n",
      "[[0.5242148  0.47578517]]\n",
      "判断 816\n",
      "[[0.5242148  0.47578517]]\n",
      "判断 819\n",
      "[[0.5242148  0.47578517]]\n",
      "判断 822\n",
      "[[0.5140747  0.48592532]]\n",
      "判断 825\n",
      "[[0.5140747  0.48592532]]\n",
      "判断 828\n",
      "[[0.5140747  0.48592532]]\n",
      "判断 831\n",
      "[[0.52074146 0.47925848]]\n",
      "判断 834\n",
      "[[0.52074146 0.47925848]]\n",
      "判断 837\n",
      "[[0.52074146 0.47925848]]\n",
      "判断 840\n",
      "[[0.51281476 0.48718527]]\n",
      "判断 843\n",
      "[[0.8270738  0.17292619]]\n",
      "判断 846\n",
      "[[0.51507044 0.48492953]]\n",
      "判断 849\n",
      "[[0.51507044 0.48492953]]\n",
      "判断 852\n",
      "[[0.51507044 0.48492953]]\n",
      "判断 855\n",
      "[[0.50769746 0.49230254]]\n",
      "判断 858\n",
      "[[0.9678541  0.03214591]]\n",
      "判断 861\n",
      "[[0.53452754 0.46547246]]\n",
      "判断 864\n",
      "[[0.53452754 0.46547246]]\n",
      "判断 867\n",
      "[[0.53452754 0.46547246]]\n",
      "判断 870\n",
      "[[0.53452754 0.46547246]]\n",
      "判断 873\n",
      "[[0.51831794 0.4816821 ]]\n",
      "判断 876\n",
      "[[0.51831794 0.4816821 ]]\n",
      "判断 879\n",
      "[[0.51831794 0.4816821 ]]\n",
      "判断 882\n",
      "[[0.51831794 0.4816821 ]]\n",
      "判断 885\n",
      "[[0.5271894  0.47281066]]\n",
      "判断 888\n",
      "[[0.5103494  0.48965064]]\n",
      "判断 891\n",
      "[[0.5103494  0.48965064]]\n",
      "判断 894\n",
      "[[0.49826798 0.501732  ]]\n",
      "判断 897\n",
      "[[0.49826798 0.501732  ]]\n",
      "判断 900\n",
      "[[0.49826798 0.501732  ]]\n",
      "判断 903\n",
      "[[0.49826798 0.501732  ]]\n",
      "判断 906\n",
      "[[0.49143663 0.5085634 ]]\n",
      "判断 909\n",
      "[[0.49110144 0.50889856]]\n",
      "判断 912\n",
      "[[0.49110144 0.50889856]]\n",
      "判断 915\n",
      "[[0.48717856 0.51282144]]\n",
      "判断 918\n",
      "[[0.48717856 0.51282144]]\n",
      "判断 921\n",
      "[[0.74602586 0.2539741 ]]\n",
      "判断 924\n",
      "[[0.48481616 0.5151838 ]]\n",
      "判断 927\n",
      "[[0.7891698  0.21083024]]\n",
      "判断 930\n",
      "[[0.48449057 0.5155094 ]]\n",
      "判断 933\n",
      "[[0.48449057 0.5155094 ]]\n",
      "判断 936\n",
      "[[0.485406 0.514594]]\n",
      "判断 939\n",
      "[[0.48513576 0.51486427]]\n",
      "判断 942\n",
      "[[0.48513576 0.51486427]]\n",
      "判断 945\n",
      "[[0.48513576 0.51486427]]\n",
      "判断 948\n",
      "[[0.48513576 0.51486427]]\n",
      "判断 951\n",
      "[[0.49061987 0.5093801 ]]\n",
      "判断 954\n",
      "[[0.49122503 0.508775  ]]\n",
      "判断 957\n",
      "[[0.49122503 0.508775  ]]\n",
      "判断 960\n",
      "[[0.49122503 0.508775  ]]\n",
      "判断 963\n",
      "[[0.4961905 0.5038095]]\n",
      "判断 966\n",
      "[[0.4961905 0.5038095]]\n",
      "判断 969\n",
      "[[0.4961905 0.5038095]]\n",
      "判断 972\n",
      "[[0.49728802 0.50271195]]\n",
      "判断 975\n",
      "[[0.49728802 0.50271195]]\n",
      "判断 978\n",
      "[[0.4975336 0.5024664]]\n",
      "判断 981\n",
      "[[0.49909526 0.50090474]]\n",
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      "[[0.49909526 0.50090474]]\n",
      "判断 987\n",
      "[[0.78396213 0.21603785]]\n",
      "判断 990\n",
      "[[0.5016559  0.49834418]]\n",
      "判断 993\n",
      "[[0.5016559  0.49834418]]\n",
      "判断 996\n",
      "[[0.506233   0.49376702]]\n",
      "判断 999\n",
      "[[0.506233   0.49376702]]\n",
      "判断 1002\n",
      "[[0.50533175 0.49466822]]\n",
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      "[[0.50533175 0.49466822]]\n",
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      "[[0.50533175 0.49466822]]\n",
      "判断 1011\n",
      "[[0.50533175 0.49466822]]\n",
      "判断 1014\n",
      "[[0.5041917  0.49580833]]\n",
      "判断 1017\n",
      "[[0.5041917  0.49580833]]\n",
      "判断 1020\n",
      "[[0.8699313 0.1300687]]\n",
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      "[[0.5059205  0.49407944]]\n",
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      "[[0.5059205  0.49407944]]\n",
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      "[[0.5057985  0.49420148]]\n",
      "判断 1032\n",
      "[[0.5057985  0.49420148]]\n",
      "判断 1035\n",
      "[[0.5057985  0.49420148]]\n",
      "判断 1038\n",
      "[[0.5057985  0.49420148]]\n",
      "判断 1041\n",
      "[[0.50211644 0.49788356]]\n",
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      "[[0.50211644 0.49788356]]\n",
      "判断 1047\n",
      "[[0.5055285 0.4944715]]\n",
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      "[[0.5055285 0.4944715]]\n",
      "判断 1053\n",
      "[[0.50922936 0.49077064]]\n",
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      "[[0.50922936 0.49077064]]\n",
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      "[[0.50922936 0.49077064]]\n",
      "判断 1062\n",
      "[[0.5193823  0.48061773]]\n",
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      "[[0.5193823  0.48061773]]\n",
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      "[[0.5193823  0.48061773]]\n",
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      "[[0.5193823  0.48061773]]\n",
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      "[[0.51461834 0.48538172]]\n",
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      "[[0.5153546  0.48464546]]\n",
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      "[[0.5153546  0.48464546]]\n",
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      "[[0.5153546  0.48464546]]\n",
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      "[[0.5153546  0.48464546]]\n",
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      "[[0.91626954 0.08373041]]\n",
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      "[[0.48722938 0.51277065]]\n",
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      "[[0.48722938 0.51277065]]\n",
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      "[[0.48722938 0.51277065]]\n",
      "判断 1101\n",
      "[[0.48722938 0.51277065]]\n",
      "判断 1104\n",
      "[[0.48938945 0.5106106 ]]\n",
      "判断 1107\n",
      "[[0.48938945 0.5106106 ]]\n",
      "判断 1110\n",
      "[[0.49016795 0.5098321 ]]\n",
      "判断 1113\n",
      "[[0.49016795 0.5098321 ]]\n",
      "判断 1116\n",
      "[[0.49016795 0.5098321 ]]\n",
      "判断 1119\n",
      "[[0.49016795 0.5098321 ]]\n",
      "判断 1122\n",
      "[[0.49118784 0.5088122 ]]\n",
      "判断 1125\n",
      "[[0.4913454 0.5086546]]\n",
      "判断 1128\n",
      "[[0.4913454 0.5086546]]\n",
      "判断 1131\n",
      "[[0.4902362  0.50976384]]\n",
      "判断 1134\n",
      "[[0.48968932 0.5103107 ]]\n",
      "判断 1137\n",
      "[[0.48968932 0.5103107 ]]\n",
      "判断 1140\n",
      "[[0.48968932 0.5103107 ]]\n",
      "判断 1143\n",
      "[[0.4895671 0.5104329]]\n",
      "判断 1146\n",
      "[[0.4895671 0.5104329]]\n",
      "判断 1149\n",
      "[[0.4878 0.5122]]\n",
      "判断 1152\n",
      "[[0.48679465 0.51320535]]\n",
      "判断 1155\n",
      "[[0.48679465 0.51320535]]\n",
      "判断 1158\n",
      "[[0.98561966 0.01438037]]\n",
      "判断 1161\n",
      "[[0.48100835 0.51899165]]\n",
      "判断 1164\n",
      "[[0.48100835 0.51899165]]\n",
      "判断 1167\n",
      "[[0.73239124 0.26760876]]\n",
      "判断 1170\n",
      "[[0.48240092 0.51759905]]\n",
      "判断 1173\n",
      "[[0.48240092 0.51759905]]\n",
      "判断 1176\n",
      "[[0.48240092 0.51759905]]\n",
      "判断 1179\n",
      "[[0.48240092 0.51759905]]\n",
      "判断 1182\n",
      "[[0.47517484 0.52482516]]\n",
      "判断 1185\n",
      "[[0.47517484 0.52482516]]\n",
      "判断 1188\n",
      "[[0.47688296 0.523117  ]]\n",
      "判断 1191\n",
      "[[0.4780749 0.5219251]]\n",
      "判断 1194\n",
      "[[0.4780749 0.5219251]]\n",
      "判断 1197\n",
      "[[0.4780749 0.5219251]]\n",
      "判断 1200\n",
      "[[0.4780749 0.5219251]]\n",
      "判断 1203\n",
      "[[0.47877 0.52123]]\n",
      "判断 1206\n",
      "[[0.47851047 0.5214895 ]]\n",
      "判断 1209\n",
      "[[0.47851047 0.5214895 ]]\n",
      "判断 1212\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1215\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1218\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1221\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1224\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1227\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1230\n",
      "[[0.47832587 0.52167416]]\n",
      "判断 1233\n",
      "[[0.75810623 0.2418937 ]]\n",
      "判断 1236\n",
      "[[0.48295984 0.51704013]]\n",
      "判断 1239\n",
      "[[0.48358038 0.51641965]]\n",
      "判断 1242\n",
      "[[0.48358038 0.51641965]]\n",
      "判断 1245\n",
      "[[0.48358038 0.51641965]]\n",
      "判断 1248\n",
      "[[0.48358038 0.51641965]]\n",
      "判断 1251\n",
      "[[0.48358038 0.51641965]]\n",
      "判断 1254\n",
      "[[0.48515555 0.5148444 ]]\n",
      "判断 1257\n",
      "[[0.48515555 0.5148444 ]]\n",
      "判断 1260\n",
      "[[0.48513594 0.514864  ]]\n",
      "判断 1263\n",
      "[[0.48523045 0.51476955]]\n",
      "判断 1266\n",
      "[[0.48523045 0.51476955]]\n",
      "判断 1269\n",
      "[[0.48583263 0.51416737]]\n",
      "判断 1272\n",
      "[[0.48583263 0.51416737]]\n",
      "判断 1275\n",
      "[[0.48583263 0.51416737]]\n",
      "判断 1278\n",
      "[[0.48140445 0.5185955 ]]\n",
      "判断 1281\n",
      "[[0.48140445 0.5185955 ]]\n",
      "判断 1284\n",
      "[[0.48140445 0.5185955 ]]\n",
      "判断 1287\n",
      "[[0.48140445 0.5185955 ]]\n",
      "判断 1290\n",
      "[[0.48389858 0.5161014 ]]\n",
      "判断 1293\n",
      "[[0.488121 0.511879]]\n",
      "判断 1296\n",
      "[[0.488121 0.511879]]\n",
      "判断 1299\n",
      "[[0.48531285 0.5146872 ]]\n",
      "判断 1302\n",
      "[[0.48531285 0.5146872 ]]\n",
      "判断 1305\n",
      "[[0.48531285 0.5146872 ]]\n",
      "判断 1308\n",
      "[[0.48568693 0.5143131 ]]\n",
      "判断 1311\n",
      "[[0.48568693 0.5143131 ]]\n",
      "判断 1314\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1317\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1320\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1323\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1326\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1329\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1332\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1335\n",
      "[[0.4851546  0.51484543]]\n",
      "判断 1338\n",
      "[[0.50482786 0.4951722 ]]\n",
      "判断 1341\n",
      "[[0.50482786 0.4951722 ]]\n",
      "判断 1344\n",
      "[[0.50482786 0.4951722 ]]\n",
      "判断 1347\n",
      "[[0.97849107 0.02150895]]\n",
      "判断 1350\n",
      "[[0.5293194  0.47068062]]\n",
      "判断 1353\n",
      "[[0.5293194  0.47068062]]\n",
      "判断 1356\n",
      "[[0.5177139  0.48228616]]\n",
      "判断 1359\n",
      "[[0.5177139  0.48228616]]\n",
      "判断 1362\n",
      "[[0.5177139  0.48228616]]\n",
      "判断 1365\n",
      "[[0.5177139  0.48228616]]\n",
      "判断 1368\n",
      "[[0.5179172 0.4820828]]\n",
      "判断 1371\n",
      "[[0.5179172 0.4820828]]\n",
      "判断 1374\n",
      "[[0.5179172 0.4820828]]\n",
      "判断 1377\n",
      "[[0.5009967  0.49900323]]\n",
      "判断 1380\n",
      "[[0.51837456 0.48162538]]\n",
      "判断 1383\n",
      "[[0.51837456 0.48162538]]\n",
      "判断 1386\n",
      "[[0.5316283 0.4683717]]\n",
      "判断 1389\n",
      "[[0.5316283 0.4683717]]\n",
      "判断 1392\n",
      "[[0.51666933 0.48333064]]\n",
      "判断 1395\n",
      "[[0.51666933 0.48333064]]\n",
      "判断 1398\n",
      "[[0.51638293 0.483617  ]]\n",
      "判断 1401\n",
      "[[0.51638293 0.483617  ]]\n",
      "判断 1404\n",
      "[[0.51638293 0.483617  ]]\n",
      "判断 1407\n",
      "[[0.5060742  0.49392584]]\n",
      "判断 1410\n",
      "[[0.5060742  0.49392584]]\n",
      "判断 1413\n",
      "[[0.5111057 0.4888943]]\n",
      "判断 1416\n",
      "[[0.5111057 0.4888943]]\n",
      "判断 1419\n",
      "[[0.55074155 0.44925848]]\n",
      "判断 1422\n",
      "[[0.55074155 0.44925848]]\n",
      "判断 1425\n",
      "[[0.5883663  0.41163373]]\n",
      "判断 1428\n",
      "[[0.5883663  0.41163373]]\n",
      "判断 1431\n",
      "[[0.5883663  0.41163373]]\n",
      "判断 1434\n",
      "[[0.58283347 0.41716656]]\n",
      "判断 1437\n",
      "[[0.58283347 0.41716656]]\n",
      "判断 1440\n",
      "[[0.4838552 0.5161448]]\n",
      "判断 1443\n",
      "[[0.4838552 0.5161448]]\n",
      "判断 1446\n",
      "[[0.4823091  0.51769096]]\n",
      "判断 1449\n",
      "[[0.4823091  0.51769096]]\n",
      "判断 1452\n",
      "[[0.4823091  0.51769096]]\n",
      "判断 1455\n",
      "[[0.48425117 0.5157488 ]]\n",
      "判断 1458\n",
      "[[0.48425117 0.5157488 ]]\n",
      "判断 1461\n",
      "[[0.8901175 0.1098825]]\n",
      "判断 1464\n",
      "[[0.48626807 0.5137319 ]]\n",
      "判断 1467\n",
      "[[0.48626807 0.5137319 ]]\n",
      "判断 1470\n",
      "[[0.48626807 0.5137319 ]]\n",
      "判断 1473\n",
      "[[0.48626807 0.5137319 ]]\n",
      "判断 1476\n",
      "[[0.7299873  0.27001265]]\n",
      "判断 1479\n",
      "[[0.501347 0.498653]]\n",
      "判断 1482\n",
      "[[0.501347 0.498653]]\n",
      "判断 1485\n",
      "[[0.49753618 0.5024639 ]]\n",
      "判断 1488\n",
      "[[0.49753618 0.5024639 ]]\n",
      "判断 1491\n",
      "[[0.8870717  0.11292829]]\n",
      "判断 1494\n",
      "[[0.50167286 0.49832714]]\n",
      "判断 1497\n",
      "[[0.50167286 0.49832714]]\n",
      "判断 1500\n",
      "[[0.50167286 0.49832714]]\n",
      "判断 1503\n",
      "[[0.5175623 0.4824377]]\n",
      "判断 1506\n",
      "[[0.51721144 0.48278856]]\n",
      "判断 1509\n",
      "[[0.51721144 0.48278856]]\n",
      "判断 1512\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.51721144 0.48278856]]\n",
      "判断 1515\n",
      "[[0.5887248  0.41127515]]\n",
      "判断 1518\n",
      "[[0.5344186  0.46558142]]\n",
      "判断 1521\n",
      "[[0.5344186  0.46558142]]\n",
      "判断 1524\n",
      "[[0.5344186  0.46558142]]\n",
      "判断 1527\n",
      "[[0.52517885 0.4748212 ]]\n",
      "判断 1530\n",
      "[[0.52517885 0.4748212 ]]\n",
      "判断 1533\n",
      "[[0.5043752 0.4956248]]\n",
      "判断 1536\n",
      "[[0.5043752 0.4956248]]\n",
      "判断 1539\n",
      "[[0.7266988  0.27330115]]\n",
      "判断 1542\n",
      "[[0.50961673 0.49038327]]\n",
      "判断 1545\n",
      "[[0.50961673 0.49038327]]\n",
      "判断 1548\n",
      "[[0.50961673 0.49038327]]\n",
      "判断 1551\n",
      "[[0.50961673 0.49038327]]\n",
      "判断 1554\n",
      "[[0.83629304 0.163707  ]]\n",
      "判断 1557\n",
      "[[0.5126674 0.4873326]]\n",
      "判断 1560\n",
      "[[0.5126674 0.4873326]]\n",
      "判断 1563\n",
      "[[0.5133541 0.4866459]]\n",
      "判断 1566\n",
      "[[0.5133541 0.4866459]]\n",
      "判断 1569\n",
      "[[0.5133541 0.4866459]]\n",
      "判断 1572\n",
      "[[0.51384 0.48616]]\n",
      "判断 1575\n",
      "[[0.51384 0.48616]]\n",
      "判断 1578\n",
      "[[0.5088424  0.49115756]]\n",
      "判断 1581\n",
      "[[0.5088424  0.49115756]]\n",
      "判断 1584\n",
      "[[0.5097817  0.49021825]]\n",
      "判断 1587\n",
      "[[0.5097817  0.49021825]]\n",
      "判断 1590\n",
      "[[0.5097817  0.49021825]]\n",
      "判断 1593\n",
      "[[0.5099219  0.49007806]]\n",
      "判断 1596\n",
      "[[0.5099219  0.49007806]]\n",
      "判断 1599\n",
      "[[0.51278806 0.4872119 ]]\n",
      "判断 1602\n",
      "[[0.51902306 0.48097694]]\n",
      "判断 1605\n",
      "[[0.51902306 0.48097694]]\n",
      "判断 1608\n",
      "[[0.51902306 0.48097694]]\n",
      "判断 1611\n",
      "[[0.51561546 0.48438457]]\n",
      "判断 1614\n",
      "[[0.51561546 0.48438457]]\n",
      "判断 1617\n",
      "[[0.51561546 0.48438457]]\n",
      "判断 1620\n",
      "[[0.5136242  0.48637584]]\n",
      "判断 1623\n",
      "[[0.512213   0.48778707]]\n",
      "判断 1626\n",
      "[[0.512213   0.48778707]]\n",
      "判断 1629\n",
      "[[0.512213   0.48778707]]\n",
      "判断 1632\n",
      "[[0.512347 0.487653]]\n",
      "判断 1635\n",
      "[[0.512347 0.487653]]\n",
      "判断 1638\n",
      "[[0.512347 0.487653]]\n",
      "判断 1641\n",
      "[[0.512347 0.487653]]\n",
      "判断 1644\n",
      "[[0.50733507 0.4926649 ]]\n",
      "判断 1647\n",
      "[[0.50733507 0.4926649 ]]\n",
      "判断 1650\n",
      "[[0.51180166 0.48819837]]\n",
      "判断 1653\n",
      "[[0.5004487  0.49955133]]\n",
      "判断 1656\n",
      "[[0.5004487  0.49955133]]\n",
      "判断 1659\n",
      "[[0.5004487  0.49955133]]\n",
      "判断 1662\n",
      "[[0.5004487  0.49955133]]\n",
      "判断 1665\n",
      "[[0.5071503 0.4928497]]\n",
      "判断 1668\n",
      "[[0.54162604 0.45837396]]\n",
      "判断 1671\n",
      "[[0.9742415  0.02575848]]\n",
      "判断 1674\n",
      "[[0.6060137 0.3939863]]\n",
      "判断 1677\n",
      "[[0.6060137 0.3939863]]\n",
      "判断 1680\n",
      "[[0.61857224 0.38142774]]\n",
      "判断 1683\n",
      "[[0.61857224 0.38142774]]\n",
      "判断 1686\n",
      "[[0.61857224 0.38142774]]\n",
      "判断 1689\n",
      "[[0.5885636 0.4114364]]\n",
      "判断 1692\n",
      "[[0.5885636 0.4114364]]\n",
      "判断 1695\n",
      "[[0.55618495 0.44381508]]\n",
      "判断 1698\n",
      "[[0.55618495 0.44381508]]\n",
      "判断 1701\n",
      "[[0.55618495 0.44381508]]\n",
      "判断 1704\n",
      "[[0.54028046 0.45971957]]\n",
      "判断 1707\n",
      "[[0.54028046 0.45971957]]\n",
      "判断 1710\n",
      "[[0.48763528 0.51236475]]\n",
      "判断 1713\n",
      "[[0.48763528 0.51236475]]\n",
      "判断 1716\n",
      "[[0.5035182  0.49648172]]\n",
      "判断 1719\n",
      "[[0.50029993 0.49970007]]\n",
      "判断 1722\n",
      "[[0.50029993 0.49970007]]\n",
      "判断 1725\n",
      "[[0.50029993 0.49970007]]\n",
      "判断 1728\n",
      "[[0.48943022 0.5105698 ]]\n",
      "判断 1731\n",
      "[[0.48943022 0.5105698 ]]\n",
      "判断 1734\n",
      "[[0.50762635 0.49237368]]\n",
      "判断 1737\n",
      "[[0.501504   0.49849606]]\n",
      "判断 1740\n",
      "[[0.501504   0.49849606]]\n",
      "判断 1743\n",
      "[[0.9661726  0.03382745]]\n",
      "判断 1746\n",
      "[[0.5105504 0.4894496]]\n",
      "判断 1749\n",
      "[[0.5105504 0.4894496]]\n",
      "判断 1752\n",
      "[[0.5111412  0.48885882]]\n",
      "判断 1755\n",
      "[[0.5111412  0.48885882]]\n",
      "判断 1758\n",
      "[[0.504986   0.49501398]]\n",
      "判断 1761\n",
      "[[0.504986   0.49501398]]\n",
      "判断 1764\n",
      "[[0.504986   0.49501398]]\n",
      "判断 1767\n",
      "[[0.48248035 0.5175197 ]]\n",
      "判断 1770\n",
      "[[0.48248035 0.5175197 ]]\n",
      "判断 1773\n",
      "[[0.48248035 0.5175197 ]]\n",
      "判断 1776\n",
      "[[0.48248035 0.5175197 ]]\n",
      "判断 1779\n",
      "[[0.48432142 0.51567864]]\n",
      "判断 1782\n",
      "[[0.48432142 0.51567864]]\n",
      "判断 1785\n",
      "[[0.48432142 0.51567864]]\n",
      "判断 1788\n",
      "[[0.9048747  0.09512529]]\n",
      "判断 1791\n",
      "[[0.4872865 0.5127135]]\n",
      "判断 1794\n",
      "[[0.4872865 0.5127135]]\n",
      "判断 1797\n",
      "[[0.48620278 0.5137972 ]]\n",
      "判断 1800\n",
      "[[0.48620278 0.5137972 ]]\n",
      "判断 1803\n",
      "[[0.48620278 0.5137972 ]]\n",
      "判断 1806\n",
      "[[0.48620278 0.5137972 ]]\n",
      "判断 1809\n",
      "[[0.4875755 0.5124245]]\n",
      "判断 1812\n",
      "[[0.4875755 0.5124245]]\n",
      "判断 1815\n",
      "[[0.4917753 0.5082247]]\n",
      "判断 1818\n",
      "[[0.49145862 0.50854135]]\n",
      "判断 1821\n",
      "[[0.49145862 0.50854135]]\n",
      "判断 1824\n",
      "[[0.48903012 0.5109699 ]]\n",
      "判断 1827\n",
      "[[0.48862907 0.5113709 ]]\n",
      "判断 1830\n",
      "[[0.48862907 0.5113709 ]]\n",
      "判断 1833\n",
      "[[0.48862907 0.5113709 ]]\n",
      "判断 1836\n",
      "[[0.49045476 0.5095452 ]]\n",
      "判断 1839\n",
      "[[0.49045476 0.5095452 ]]\n",
      "判断 1842\n",
      "[[0.89148074 0.10851923]]\n",
      "判断 1845\n",
      "[[0.47936875 0.5206312 ]]\n",
      "判断 1848\n",
      "[[0.47936875 0.5206312 ]]\n",
      "判断 1851\n",
      "[[0.47936875 0.5206312 ]]\n",
      "判断 1854\n",
      "[[0.47936875 0.5206312 ]]\n",
      "判断 1857\n",
      "[[0.48354325 0.5164568 ]]\n",
      "判断 1860\n",
      "[[0.47972575 0.5202742 ]]\n",
      "判断 1863\n",
      "[[0.47972575 0.5202742 ]]\n",
      "判断 1866\n",
      "[[0.4809302 0.5190698]]\n",
      "判断 1869\n",
      "[[0.4809302 0.5190698]]\n",
      "判断 1872\n",
      "[[0.9122191  0.08778087]]\n",
      "判断 1875\n",
      "[[0.47968468 0.52031535]]\n",
      "判断 1878\n",
      "[[0.47968468 0.52031535]]\n",
      "判断 1881\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1884\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1887\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1890\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1893\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1896\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1899\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1902\n",
      "[[0.48643702 0.513563  ]]\n",
      "判断 1905\n",
      "[[0.51464003 0.48536   ]]\n",
      "判断 1908\n",
      "[[0.5252128  0.47478715]]\n",
      "判断 1911\n",
      "[[0.5252128  0.47478715]]\n",
      "判断 1914\n",
      "[[0.52248925 0.47751078]]\n",
      "判断 1917\n",
      "[[0.52248925 0.47751078]]\n",
      "判断 1920\n",
      "[[0.53766304 0.462337  ]]\n",
      "判断 1923\n",
      "[[0.5139142  0.48608574]]\n",
      "判断 1926\n",
      "[[0.5139142  0.48608574]]\n",
      "判断 1929\n",
      "[[0.8767883  0.12321173]]\n",
      "判断 1932\n",
      "[[0.50969267 0.49030733]]\n",
      "判断 1935\n",
      "[[0.50969267 0.49030733]]\n",
      "判断 1938\n",
      "[[0.5776906  0.42230937]]\n",
      "判断 1941\n",
      "[[0.5776906  0.42230937]]\n",
      "判断 1944\n",
      "[[0.5620252  0.43797484]]\n",
      "判断 1947\n",
      "[[0.5620252  0.43797484]]\n",
      "判断 1950\n",
      "[[0.5620252  0.43797484]]\n",
      "判断 1953\n",
      "[[0.50780153 0.49219844]]\n",
      "判断 1956\n",
      "[[0.50780153 0.49219844]]\n",
      "判断 1959\n",
      "[[0.50780153 0.49219844]]\n",
      "判断 1962\n",
      "[[0.50780153 0.49219844]]\n",
      "判断 1965\n",
      "[[0.49802437 0.5019756 ]]\n",
      "判断 1968\n",
      "[[0.49802437 0.5019756 ]]\n",
      "判断 1971\n",
      "[[0.49802437 0.5019756 ]]\n",
      "判断 1974\n",
      "[[0.9763727  0.02362728]]\n",
      "判断 1977\n",
      "[[0.4885371  0.51146287]]\n",
      "判断 1980\n",
      "[[0.4885371  0.51146287]]\n",
      "判断 1983\n",
      "[[0.51503175 0.48496822]]\n",
      "判断 1986\n",
      "[[0.51503175 0.48496822]]\n",
      "判断 1989\n",
      "[[0.51503175 0.48496822]]\n",
      "判断 1992\n",
      "[[0.51503175 0.48496822]]\n",
      "判断 1995\n",
      "[[0.53076553 0.46923444]]\n",
      "判断 1998\n",
      "[[0.5364581  0.46354187]]\n",
      "判断 2001\n",
      "[[0.5364581  0.46354187]]\n",
      "判断 2004\n",
      "[[0.5364581  0.46354187]]\n",
      "判断 2007\n",
      "[[0.5231742  0.47682577]]\n",
      "判断 2010\n",
      "[[0.5231742  0.47682577]]\n",
      "判断 2013\n",
      "[[0.53319263 0.4668074 ]]\n",
      "判断 2016\n",
      "[[0.53319263 0.4668074 ]]\n",
      "判断 2019\n",
      "[[0.50912595 0.49087402]]\n",
      "判断 2022\n",
      "[[0.50912595 0.49087402]]\n",
      "判断 2025\n",
      "[[0.48337647 0.5166235 ]]\n",
      "判断 2028\n",
      "[[0.48337647 0.5166235 ]]\n",
      "判断 2031\n",
      "[[0.48364288 0.5163571 ]]\n",
      "判断 2034\n",
      "[[0.48364288 0.5163571 ]]\n",
      "判断 2037\n",
      "[[0.7890701  0.21092983]]\n",
      "判断 2040\n",
      "[[0.48126137 0.5187386 ]]\n",
      "判断 2043\n",
      "[[0.48126137 0.5187386 ]]\n",
      "判断 2046\n",
      "[[0.48126137 0.5187386 ]]\n",
      "判断 2049\n",
      "[[0.48126137 0.5187386 ]]\n",
      "判断 2052\n",
      "[[0.48451725 0.5154827 ]]\n",
      "判断 2055\n",
      "[[0.48451725 0.5154827 ]]\n",
      "判断 2058\n",
      "[[0.4864219 0.5135781]]\n",
      "判断 2061\n",
      "[[0.51321894 0.486781  ]]\n",
      "判断 2064\n",
      "[[0.51321894 0.486781  ]]\n",
      "判断 2067\n",
      "[[0.51321894 0.486781  ]]\n",
      "判断 2070\n",
      "[[0.51321894 0.486781  ]]\n",
      "判断 2073\n",
      "[[0.5166579  0.48334214]]\n",
      "判断 2076\n",
      "[[0.5094891  0.49051088]]\n",
      "判断 2079\n",
      "[[0.5094891  0.49051088]]\n",
      "判断 2082\n",
      "[[0.5058764  0.49412358]]\n",
      "判断 2085\n",
      "[[0.5058764  0.49412358]]\n",
      "判断 2088\n",
      "[[0.897365   0.10263503]]\n",
      "判断 2091\n",
      "[[0.5060019 0.4939981]]\n",
      "判断 2094\n",
      "[[0.5060019 0.4939981]]\n",
      "判断 2097\n",
      "[[0.5060019 0.4939981]]\n",
      "判断 2100\n",
      "[[0.51023704 0.48976296]]\n",
      "判断 2103\n",
      "[[0.786979 0.213021]]\n",
      "判断 2106\n",
      "[[0.51692754 0.48307243]]\n",
      "判断 2109\n",
      "[[0.5159076  0.48409244]]\n",
      "判断 2112\n",
      "[[0.5159076  0.48409244]]\n",
      "判断 2115\n",
      "[[0.5159076  0.48409244]]\n",
      "判断 2118\n",
      "[[0.5040886 0.4959114]]\n",
      "判断 2121\n",
      "[[0.49068266 0.5093174 ]]\n",
      "判断 2124\n",
      "[[0.49296674 0.5070332 ]]\n",
      "判断 2127\n",
      "[[0.49296674 0.5070332 ]]\n",
      "判断 2130\n",
      "[[0.49296674 0.5070332 ]]\n",
      "判断 2133\n",
      "[[0.48558283 0.5144172 ]]\n",
      "判断 2136\n",
      "[[0.48558283 0.5144172 ]]\n",
      "判断 2139\n",
      "[[0.48620808 0.5137919 ]]\n",
      "判断 2142\n",
      "[[0.48620808 0.5137919 ]]\n",
      "判断 2145\n",
      "ipcam stopped!\n"
     ]
    }
   ],
   "source": [
    "# URL = \"rtsp://admin:admin@192.168.1.1/video.h264\"\n",
    "URL = \"rtsp://admin:RCDPLD@192.168.0.110:554/h264/ch1/main/av_stream\"\n",
    "\n",
    "# 連接攝影機\n",
    "ipcam = ipcamCapture(URL)\n",
    "\n",
    "# 啟動子執行緒\n",
    "ipcam.start()\n",
    "\n",
    "time.sleep(1)\n",
    "\n",
    "i = 0\n",
    "data_list = []\n",
    "raw_data_list = []  # 网络每次输入的第一帧为原始图像\n",
    "while True:\n",
    "    ret, I = ipcam.getframe()\n",
    "    start = time.time()\n",
    "    \n",
    "    cv2.namedWindow(\"Image\", cv2.WINDOW_NORMAL)\n",
    "    if ret:\n",
    "        frame = cv2.resize(I, (224, 224))\n",
    "        if i == 0:  # 第一帧直接放入\n",
    "            data_list.append(frame)\n",
    "        elif (i+1) % 3 == 0:  # 缓存第3i-1帧\n",
    "            temp_frame = frame\n",
    "        elif i % 3 == 0:  # 每3帧取一次，减去前一帧\n",
    "            # 保留原始帧作为后续输入的第一帧\n",
    "            raw_data_list.append(frame)\n",
    "            data_list.append(frame - temp_frame)\n",
    "        # print(len(data_list))\n",
    "        # 当data放满一组4帧，开始处理数据\n",
    "        if len(data_list)==2:\n",
    "            # 处理数据\n",
    "            np_data = np.array(data_list).astype(np.float32)\n",
    "            norm_data = normalize(np_data)\n",
    "            input_data = norm_data[np.newaxis, ...]\n",
    "#             input_data = np_data[np.newaxis, ...]\n",
    "            # 输入到网络\n",
    "            print(model.predict(input_data))\n",
    "            print(\"判断\",i)\n",
    "            data_list.pop(0)\n",
    "            data_list[0] = raw_data_list.pop(0)\n",
    "            \n",
    "        i += 1    \n",
    "        cv2.imshow(\"Image\", I)\n",
    "        \n",
    "    if cv2.waitKey(1) == 113:\n",
    "        cv2.destroyAllWindows()\n",
    "        ipcam.stop()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T10:18:29.564070Z",
     "start_time": "2020-04-17T10:18:29.558331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(max(33-500,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T11:59:26.832087Z",
     "start_time": "2020-04-17T11:59:26.569162Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-dd8bb4f53e29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# plt.imshow(data_list[0])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_data_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data_list' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plt.imshow(data_list[0])\n",
    "print(np.array(data_list).shape)\n",
    "print(len(data_list))\n",
    "print(len(raw_data_list))\n",
    "np.array(data_list)[np.newaxis, ...].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T11:30:03.885820Z",
     "start_time": "2020-04-17T11:30:03.728215Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T09:06:20.671249Z",
     "start_time": "2020-04-17T09:06:20.658947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 2] [12]\n"
     ]
    }
   ],
   "source": [
    "list_1 = [0,1,2]\n",
    "list_2 = [11,12]\n",
    "\n",
    "list_1.pop(0)\n",
    "list_1[0] = list_2.pop(0)\n",
    "print(list_1,list_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T08:51:33.464474Z",
     "start_time": "2020-04-17T08:51:33.286633Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f17641f1850>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data_list[16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-20T09:06:58.707427Z",
     "start_time": "2020-04-20T09:06:58.704676Z"
    }
   },
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
